Exploiting And Estimating Malware Using Feature Impact Derived From API Call Sequence Learning

Malware is a serious threat being posed and it has been a continuous process of protecting the systems from existing and new malware variants by defining new approaches for malware detection .In this process malware samples are first analyzed to understand the behavior of the vulnerable samples and accordingly statistical methods are defined for malware detection. Many approaches are defined for understanding the behavior of malware executables which are broadly classified in to static and dynamic assessments. The static analysis can only be used for identifying the existing types of malware but code obfuscation has made it complex to identify the variants of existing malware. To counter the code obfuscation the dynamic analysis of malware is prioritized over static analysis where the malwares are analyzed by running them in an emulated environment to understand the intent of the samples. As there is an acute need of developing a more precise and accurate approach for malware detection, this paper contributes in the above said direction where we proposed a novel measure to estimate malware by exploiting the malicious intent of executables. It is a machine learning approach where the knowledge is acquired from the existing malicious executable and the same knowledge is used to estimate the new variants of the existing malware. The proposed statistical approach can be used to improve the scalability, accuracy and robustness. It also defends against zero day exploits

Evaluation of Elderly Fall Detection Systems using Data Analysis

As the global population continues to age, it is more important to ensure the safety and wellbeing of senior citizens. Falls are a frequent and dangerous concern for elders, frequently resulting in fatalities or very serious injuries. Modern technology, such as the Elderly Fall Detection System, have been created to solve this issue. This technology attempts to detect falls quickly and react to them, offering aid right away and maybe saving lives. These studies identify a fall when the tri-axial accelerometer reading from a wearable device exceeds the predetermined threshold. Less complexity and computational expense compared to other approaches is one of the main benefits of adopting threshold-based methods. Finding the right threshold value, however, to accurately identify all falls without confounding them with certain ADL, has proven to be a challenging issue. Early Intervention and Medical Aid: Being independent and able to live in one's own house are two things that many elderly people enjoy. Support for carers and peace of mind: detection systems not only aid the elderly but also provide their carers peace of mind. Cost-cutting: Falls among the elderly frequently lead to hospital stays, rehab, and higher healthcare expenses. The Weighted Product Model (WPM) is a technique for ranking and evaluating alternatives based on a variety of factors. It is an easy and obvious method that enables decision-makers to weigh the relative weight of many variables and make wise decisions. Smart surveillance system, Smart cane, Smart Carpet, Smart phone/watch and app. Unobtrusiveness, reliability, privacy, Cost. As the global population continues to age, it is more important to ensure the safety and wellbeing of senior citizens. Falls are a frequent and dangerous concern for elders, frequently resulting in fatalities or very serious injuries The Weighted Product Model (WPM) is a technique for ranking and evaluating alternatives based on a variety of factors Unobtrusiveness, reliability, privacy, Cost, smart phone is the highest and smart carpet is the lowes

Ethnobotanical Observations on Some Endemic Plants of Eastern Ghats, India

The present paper reports ethnobotanical usage of 28 endemic plant species used by the tribes of Eastern Ghats, India. All the species were enumerated with botanical name, family name, vernacular name, habit, habitat and information on ethnic uses

FLBP: A Federated Learning-enabled and Blockchain-supported Privacy-Preserving of Electronic Patient Records for the Internet of Medical Things

The evolution of the computing paradigms and the Internet of Medical Things (IoMT) have transfigured the healthcare sector with an alarming rise of privacy issues in healthcare records. The rapid growth of medical data leads to privacy and security concerns to protect the confidentiality and integrity of the data in the feature-loaded infrastructure and applications. Moreover, the sharing of medical records of a patient among hospitals rises security and interoperability issues. This article, therefore, proposes a Federated Learning-and-Blockchain-enabled framework to protect electronic medical records from unauthorized access using a deep learning technique called Artificial Neural Network (ANN) for a collaborative IoMT-Fog-Cloud environment. ANN is used to identify insiders and intruders. An Elliptical Curve Digital Signature (ECDS) algorithm is adopted to devise a secured Blockchain-based validation method. To process the anti-malicious propagation method, a Blockchain-based Health Record Sharing (BHRS) is implemented. In addition, an FL approach is integrated into Blockchain for scalable applications to form a global model without the need of sharing and storing the raw data in the Cloud. The proposed model is evident from the simulations that it improves the operational cost and communication (latency) overhead with a percentage of 85.2% and 62.76%, respectively. The results showcase the utility and efficacy of the proposed model

Data Mining with Supervised Instance Selection Improves Artificial Neural Network Classification Accuracy

IDSs may monitor intrusion logs, traffic control packets, and assaults. Nets create large amounts of data. IDS log characteristics are used to detect whether a record or connection was attacked or regular network activity. Reduced feature size aids machine learning classification. This paper describes a standardised and systematic intrusion detection classification approach. Using dataset signatures, the Naive Bayes Algorithm, Random Tree, and Neural Network classifiers are assessed. We examine the feature reduction efficacy of PCA and the fisheries score in this study. The first round of testing uses a reduced dataset without decreasing the components set, and the second uses principal components analysis. PCA boosts classification accuracy by 1.66 percent. Artificial immune systems, inspired by the human immune system, use learning, long-term memory, and association to recognise and v-classify. Introduces the Artificial Neural Network (ANN) classifier model and its development issues. Iris and Wine data from the UCI learning repository proves the ANN approach works. Determine the role of dimension reduction in ANN-based classifiers. Detailed mutual information-based feature selection methods are provided. Simulations from the KDD Cup'99 demonstrate the method's efficacy. Classifying big data is important to tackle most engineering, health, science, and business challenges. Labelled data samples train a classifier model, which classifies unlabeled data samples into numerous categories. Fuzzy logic and artificial neural networks (ANNs) are used to classify data in this dissertation

Artificial Intelligence Framework for Sugarcane Diseases Classification using Convolutional neural Network

In many regions of the world, plant disorders have long been a threat to crop development and agricultural production, negatively affecting the availability of food for people. The best organised sector of agriculture is sugarcane cultivation. It is the first crop that farmers grow because of the ideal conditions for its development. It is closely related to the sugar sector and has a significant impact on the economy of several countries. Of all the crops grown for commercial purposes, sugarcane has the highest production value. In contrast, a different type of diseases can affect the quality and productivity of the crop. Growers can detect some of them by visual inspection of the leaves. Unfortunately, the majority of infections go undetected, causing farmers to suffer significant losses. To reduce the damage caused by an infestation, it is important to determine the type of infestation. So, we proposed a Deep Learning (DL) model that uses images of diseased leaves to train the model to recognise a specific disease affecting the sugarcane plant. In this work, we have used bacterial blight, red rot, red rust and healthy leaf images. The method used two convolutional neural networks to classify the 851 sugarcane leaf images. As a result, Resnet50 achieved the highest accuracy of and 99.70% for binary classification (normal and abnormal images). The trained model achieved its goal by identifying photos of sugarcane and classifying them into classes of healthy and diseased leaves. As a result, this research provides a proposal for using deep learning algorithms to help farmers detect and categorise sugarcane infections. Finally, we have applied an DL visualization technique such as gradient class activation map, occlusion sensitivity, and local interpretable model-agnostic to differentiate and understanding the classification process by highlighting area that is more used for the classification

Enhancement and Segmentation of Low-Light Images Using Illumination Map Estimation based Level Set (IME-LS) Method

One of the most difficult aspects of image segmentation is that it cannot be successfully segmented if the image is dark or degraded. In this paper, proposes a method for the segmentation of low-quality and degraded images is put forth which is called Illumination Map Estimation based Level Set (IME-LS) Method. This proposed model has been classified into two parts: Firstly, we use an enhancement approach using illumination map approximation to enhance the input image. In this approach, Illumination map is constructed then refined. The refined illumination map undergoes an Augmented Lagrangian algorithm and then we use a sped-up solver for a considerably more efficient outcome. Secondly, Then the segmentation procedure begins once the image is enhanced. The enhanced image is segmented using level set bias method through Fuzzy clustering. In this method we employ the Fuzzy C-Means (FCM) algorithm to categorize data points into clusters. The fuzzy C-Means algorithm separates different entities in the image based on their varied intensities and sorts them into various clusters. The level set bias approach then tracks the variational boundaries of the image. We have designed the integrated algorithm in such a way that the image is classified or grouped into various clusters using a novel fuzzy Clustering algorithm and the variational boundaries of those clusters are tracked by employing the level set algorithm. In this paper, we further perform quantitative and comparative analysis of the suggested technique with respect to other segmentation techniques to illustrate the efficiency and flexibility of the suggested model

CaP: Cardiovascular Disease Prediction using a Delta Layer based Center Vector Activation-centric DCNN

Cardiac disease stands as a primary contributor to mortality, representing a prevalent category of chronic and life-threatening conditions. Therefore, early detection is imperative. While existing research has sought to predict heart disease (HD) through Electrocardiogram (ECG) signals, there remains room for enhancement. This study introduces a novel approach for early HD detection based on the Delta Layer with Center Vector Activation-centric Deep Convolutional Neural Network (DLCVA-DCNN) within its research framework, namely: CaP. Initially, the input ECG signals undergo preprocessing using a Weighted Covariance Kalman Filter (WCKF) to eliminate noise. Subsequently, the preprocessed data is bifurcated: one branch transforms it into a binary image, while the other decomposes the signal to identify peak segments. The decomposition employs the Bivariate Ensemble Empirical Mode Decomposition (BEEMD) method, and the Pan-Tompkins Algorithm (PTA) is applied to ascertain the highest-frequency segments. The coupling information is then extracted from these peaks. Simultaneously, depth features are extracted from the binary image. The Linear Approximate Functional Walrus Optimization Algorithm (LAFWOA) is employed to select pertinent features from the coupling and depth features. These selected features are input into the DLCVA-DCNN classifier to discriminate disease and standard signals. The experimental analysis compares the proposed methodology with conventional frameworks based on performance metrics, revealing that the proposed approach achieves higher accuracy than existing techniques